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- Tasks: Conduct cutting-edge research in networked scalable learning and AI systems.
- Company: Brunel University London, a leading multidisciplinary research university.
- Benefits: Full PhD scholarship, competitive salary, generous leave, and excellent training opportunities.
- Other info: Hybrid working model and commitment to inclusivity in the workplace.
- Why this job: Join an innovative project with global impact and advance your career in AI research.
- Qualifications: Background in computer science, machine learning, or related fields.
The predicted salary is between 40757 - 40757 £ per year.
Research Assistant (MSCA) with a PhD full scholarship on Networked Scalable Learning – 16044
Department of Computer Science, College of Engineering, Design and Physical Sciences
Location: Brunel University of London, Uxbridge Campus
Salary: £40,757 per annum inclusive of London Allowance
The University will waive international PhD tuition fees for the duration of the program.
Hours: Full-time
Contract Type: Fixed-term for 29 months, followed by a 7-month stipend at the UKRI rate (currently approximately £1,983.75 per month, subject to adjustment in line with UKRI rates), covering the full duration of the PhD.
Brunel University of London was established in 1966 and is a leading multidisciplinary research-intensive technology university delivering economic, social and cultural benefits.
This PhD scholarship is part of the EU Horizon Europe Marie Skłodowska‑Curie Actions Doctoral Network (MSCA DN) project ANT – Embedded AI Systems and Applications. The ANT project is a large-scale international doctoral training program, offering 18 fully funded PhD positions with a total budget of €4.9 million, aimed at advancing research in embedded and networked AI systems.
Expected Results:
- Networked scalable learning techniques that accommodate the dynamic topology, computing loads, data volume, resource availability and quality of service (QoS) requirements;
- Network protocols to facilitate networked scalable learning;
- Optimisation schemes for dynamic resource allocation and computing load distribution.
Planned secondments:
- ST (3 months, M16‑M18, may be rescheduled): Networked scalable and continual learning in dynamic evolving environment
- IMEC (4 months, M27‑M30, may be rescheduled): Efficient data transfer protocols for networked scalable learning
Candidate profile:
Computer science, machine learning, electrical engineering, telecommunication engineering, applied mathematics, or related areas.
Desirable skills/interests:
- Machine learning
- Large language model
- Large AI model
- Signal processing
- Wireless communications
- Applied optimisation
We offer a generous annual leave package plus discretionary University closure days, excellent training and development opportunities, as well as a great occupational pension scheme and a range of health-related support. The University is committed to a hybrid working approach. Family allowance may be payable on eligibility and supporting evidence. Research training and network fees are available up to £50,139.84 for 3 years.
Appointment to this post is subject to the successful applicant holding the appropriate permission to work in the UK, and an ATAS certificate may be required depending on nationality. The university may provide support for applying for a Global Talent Visa.
Closing date for applications: 21 May 2026
Equal Opportunities
Brunel University of London is fully committed to creating and sustaining a fully inclusive workforce culture. We welcome applicants from all backgrounds and communities, and we particularly welcome applicants who are currently under‑represented in our workforce.
Research Assistant (MSCA) with PhD Full Scholarship employer: Brunel University London
Contact Detail:
Brunel University London Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Research Assistant (MSCA) with PhD Full Scholarship
✨Tip Number 1
Network, network, network! Reach out to current or former PhD students at Brunel University or in similar fields. They can provide insider info and might even put in a good word for you.
✨Tip Number 2
Prepare for your interview by brushing up on the latest trends in networked scalable learning and AI systems. Show us that you're not just passionate but also knowledgeable about the field!
✨Tip Number 3
Don’t forget to showcase your skills! Bring examples of your previous work or projects related to machine learning or optimisation. We love seeing how you’ve applied your knowledge in real-world scenarios.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen. Plus, it shows us you’re serious about joining our team at Brunel University.
We think you need these skills to ace Research Assistant (MSCA) with PhD Full Scholarship
Some tips for your application 🫡
Tailor Your Application: Make sure to customise your application to highlight how your skills and experiences align with the research focus of the position. We want to see your passion for networked scalable learning and how you can contribute to the ANT project.
Showcase Relevant Experience: Don’t hold back on sharing any relevant projects or research you've done in computer science, machine learning, or related fields. We love seeing practical examples that demonstrate your expertise and interest in the area!
Be Clear and Concise: Keep your writing clear and to the point. We appreciate well-structured applications that are easy to read. Avoid jargon unless it’s necessary, and make sure your enthusiasm shines through!
Apply Through Our Website: Remember to submit your application through our official website. It’s the best way to ensure we receive all your details correctly and can process your application smoothly. We can’t wait to hear from you!
How to prepare for a job interview at Brunel University London
✨Know Your Research
Dive deep into the specifics of networked scalable learning and the ANT project. Familiarise yourself with the expected results and how your background in computer science or related fields aligns with their goals. This will show your genuine interest and understanding of the role.
✨Showcase Relevant Skills
Highlight your experience with machine learning, AI models, and optimisation techniques during the interview. Prepare examples of past projects or research that demonstrate your expertise in these areas, as they are crucial for this position.
✨Prepare Questions
Think of insightful questions to ask about the PhD programme, secondments, and the team you'll be working with. This not only shows your enthusiasm but also helps you gauge if the environment is the right fit for you.
✨Practice Your Presentation
Since this role involves research, you might be asked to present your previous work or ideas. Practise explaining complex concepts clearly and concisely, as effective communication is key in academia and research settings.